Overview

Dataset statistics

Number of variables37
Number of observations1624
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory835.9 B

Variable types

Categorical24
Numeric12
Boolean1

Alerts

def_two_point_conv has constant value "0" Constant
game_id has a high cardinality: 820 distinct values High cardinality
game_date has a high cardinality: 174 distinct values High cardinality
def_int is highly correlated with Total_DKP and 1 other fieldsHigh correlation
sacks is highly correlated with Total_DKP and 1 other fieldsHigh correlation
Opponent_score is highly correlated with points_allowed_7_13 and 3 other fieldsHigh correlation
points_allowed_7_13 is highly correlated with Opponent_scoreHigh correlation
points_allowed_35 is highly correlated with Opponent_scoreHigh correlation
Total_DKP is highly correlated with def_int and 3 other fieldsHigh correlation
Total_FDP is highly correlated with def_int and 3 other fieldsHigh correlation
def_int is highly correlated with Total_DKP and 1 other fieldsHigh correlation
sacks is highly correlated with Total_DKP and 1 other fieldsHigh correlation
Opponent_score is highly correlated with points_allowed_35 and 2 other fieldsHigh correlation
points_allowed_35 is highly correlated with Opponent_scoreHigh correlation
Total_DKP is highly correlated with def_int and 3 other fieldsHigh correlation
Total_FDP is highly correlated with def_int and 3 other fieldsHigh correlation
Opponent_score is highly correlated with Total_DKP and 1 other fieldsHigh correlation
Total_DKP is highly correlated with Opponent_score and 1 other fieldsHigh correlation
Total_FDP is highly correlated with Opponent_score and 1 other fieldsHigh correlation
def_two_point_conv is highly correlated with Roof and 21 other fieldsHigh correlation
Roof is highly correlated with def_two_point_conv and 1 other fieldsHigh correlation
def_int_td is highly correlated with def_two_point_convHigh correlation
fumbles_rec_td is highly correlated with def_two_point_convHigh correlation
home_team is highly correlated with def_two_point_conv and 3 other fieldsHigh correlation
total_ret_td is highly correlated with def_two_point_convHigh correlation
points_allowed_14_20 is highly correlated with def_two_point_convHigh correlation
blocked_kick is highly correlated with def_two_point_convHigh correlation
points_allowed_1_6 is highly correlated with def_two_point_convHigh correlation
fumbles_rec is highly correlated with def_two_point_convHigh correlation
Opponent_abbrev is highly correlated with def_two_point_convHigh correlation
safety is highly correlated with def_two_point_convHigh correlation
OT is highly correlated with def_two_point_convHigh correlation
Surface is highly correlated with def_two_point_conv and 1 other fieldsHigh correlation
Vegas_Favorite is highly correlated with def_two_point_conv and 1 other fieldsHigh correlation
points_allowed_28_34 is highly correlated with def_two_point_convHigh correlation
points_allowed_0 is highly correlated with def_two_point_convHigh correlation
points_allowed_7_13 is highly correlated with def_two_point_convHigh correlation
points_allowed_21_27 is highly correlated with def_two_point_convHigh correlation
team is highly correlated with def_two_point_conv and 1 other fieldsHigh correlation
points_allowed_35 is highly correlated with def_two_point_convHigh correlation
Team_abbrev is highly correlated with def_two_point_conv and 1 other fieldsHigh correlation
vis_team is highly correlated with def_two_point_convHigh correlation
team is highly correlated with Team_abbrev and 6 other fieldsHigh correlation
def_int is highly correlated with def_int_td and 2 other fieldsHigh correlation
def_int_td is highly correlated with def_int and 2 other fieldsHigh correlation
sacks is highly correlated with Total_DKP and 1 other fieldsHigh correlation
fumbles_rec is highly correlated with Total_DKP and 1 other fieldsHigh correlation
total_ret_td is highly correlated with Total_DKP and 1 other fieldsHigh correlation
Team_abbrev is highly correlated with team and 6 other fieldsHigh correlation
Opponent_abbrev is highly correlated with team and 6 other fieldsHigh correlation
Opponent_score is highly correlated with points_allowed_0 and 10 other fieldsHigh correlation
points_allowed_0 is highly correlated with Opponent_scoreHigh correlation
points_allowed_1_6 is highly correlated with Opponent_score and 2 other fieldsHigh correlation
points_allowed_7_13 is highly correlated with Opponent_score and 3 other fieldsHigh correlation
points_allowed_14_20 is highly correlated with Opponent_score and 2 other fieldsHigh correlation
points_allowed_21_27 is highly correlated with Opponent_score and 2 other fieldsHigh correlation
points_allowed_28_34 is highly correlated with Opponent_score and 2 other fieldsHigh correlation
points_allowed_35 is highly correlated with Opponent_score and 4 other fieldsHigh correlation
Total_DKP is highly correlated with def_int and 9 other fieldsHigh correlation
Total_FDP is highly correlated with def_int and 9 other fieldsHigh correlation
vis_team is highly correlated with team and 4 other fieldsHigh correlation
home_team is highly correlated with team and 10 other fieldsHigh correlation
vis_score is highly correlated with Opponent_score and 5 other fieldsHigh correlation
home_score is highly correlated with Opponent_score and 4 other fieldsHigh correlation
Roof is highly correlated with team and 8 other fieldsHigh correlation
Surface is highly correlated with team and 5 other fieldsHigh correlation
Temperature is highly correlated with home_team and 4 other fieldsHigh correlation
Humidity is highly correlated with home_team and 3 other fieldsHigh correlation
Wind_Speed is highly correlated with home_team and 3 other fieldsHigh correlation
Vegas_Favorite is highly correlated with team and 10 other fieldsHigh correlation
Over_Under is highly correlated with home_team and 1 other fieldsHigh correlation
game_id is uniformly distributed Uniform
team is uniformly distributed Uniform
Team_abbrev is uniformly distributed Uniform
Opponent_abbrev is uniformly distributed Uniform
vis_team is uniformly distributed Uniform
home_team is uniformly distributed Uniform
def_int has 761 (46.9%) zeros Zeros
sacks has 203 (12.5%) zeros Zeros
Total_DKP has 93 (5.7%) zeros Zeros
Total_FDP has 93 (5.7%) zeros Zeros
Wind_Speed has 593 (36.5%) zeros Zeros

Reproduction

Analysis started2022-09-02 01:43:03.763017
Analysis finished2022-09-02 01:43:51.025942
Duration47.26 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

game_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct820
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Memory size109.6 KiB
201909050chi
 
2
202109160was
 
2
202109120det
 
2
202109120htx
 
2
202109120kan
 
2
Other values (815)
1614 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters19488
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)1.0%

Sample

1st row201909050chi
2nd row201909050chi
3rd row201909080car
4th row201909080car
5th row201909080cle

Common Values

ValueCountFrequency (%)
201909050chi2
 
0.1%
202109160was2
 
0.1%
202109120det2
 
0.1%
202109120htx2
 
0.1%
202109120kan2
 
0.1%
202109120nor2
 
0.1%
202109120nwe2
 
0.1%
202109120nyg2
 
0.1%
202109120oti2
 
0.1%
202109120ram2
 
0.1%
Other values (810)1604
98.8%

Length

2022-09-02T01:43:51.209287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201909050chi2
 
0.1%
201909080jax2
 
0.1%
201909260gnb2
 
0.1%
201909290atl2
 
0.1%
201909290buf2
 
0.1%
201909150ram2
 
0.1%
201909150oti2
 
0.1%
201910200sea2
 
0.1%
201909150nyg2
 
0.1%
201909080cle2
 
0.1%
Other values (810)1604
98.8%

Most occurring characters

ValueCountFrequency (%)
05226
26.8%
24008
20.6%
13402
17.5%
91005
 
5.2%
a609
 
3.1%
n471
 
2.4%
i397
 
2.0%
t356
 
1.8%
c297
 
1.5%
r296
 
1.5%
Other values (24)3421
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14616
75.0%
Lowercase Letter4872
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a609
 
12.5%
n471
 
9.7%
i397
 
8.1%
t356
 
7.3%
c297
 
6.1%
r296
 
6.1%
e249
 
5.1%
d244
 
5.0%
m203
 
4.2%
s201
 
4.1%
Other values (14)1549
31.8%
Decimal Number
ValueCountFrequency (%)
05226
35.8%
24008
27.4%
13402
23.3%
91005
 
6.9%
3225
 
1.5%
7173
 
1.2%
5162
 
1.1%
8155
 
1.1%
6137
 
0.9%
4123
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common14616
75.0%
Latin4872
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a609
 
12.5%
n471
 
9.7%
i397
 
8.1%
t356
 
7.3%
c297
 
6.1%
r296
 
6.1%
e249
 
5.1%
d244
 
5.0%
m203
 
4.2%
s201
 
4.1%
Other values (14)1549
31.8%
Common
ValueCountFrequency (%)
05226
35.8%
24008
27.4%
13402
23.3%
91005
 
6.9%
3225
 
1.5%
7173
 
1.2%
5162
 
1.1%
8155
 
1.1%
6137
 
0.9%
4123
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII19488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05226
26.8%
24008
20.6%
13402
17.5%
91005
 
5.2%
a609
 
3.1%
n471
 
2.4%
i397
 
2.0%
t356
 
1.8%
c297
 
1.5%
r296
 
1.5%
Other values (24)3421
17.6%

team
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct32
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size95.3 KiB
KAN
 
57
LAR
 
55
TAM
 
55
BUF
 
55
GNB
 
54
Other values (27)
1348 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4872
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHI
2nd rowGNB
3rd rowLAR
4th rowCAR
5th rowCLE

Common Values

ValueCountFrequency (%)
KAN57
 
3.5%
LAR55
 
3.4%
TAM55
 
3.4%
BUF55
 
3.4%
GNB54
 
3.3%
TEN54
 
3.3%
SFO54
 
3.3%
CIN53
 
3.3%
NOR52
 
3.2%
SEA52
 
3.2%
Other values (22)1083
66.7%

Length

2022-09-02T01:43:51.438827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kan57
 
3.5%
tam55
 
3.4%
buf55
 
3.4%
lar55
 
3.4%
gnb54
 
3.3%
ten54
 
3.3%
sfo54
 
3.3%
cin53
 
3.3%
nor52
 
3.2%
sea52
 
3.2%
Other values (22)1083
66.7%

Most occurring characters

ValueCountFrequency (%)
A666
13.7%
N569
 
11.7%
I405
 
8.3%
L340
 
7.0%
E306
 
6.3%
T258
 
5.3%
C252
 
5.2%
R240
 
4.9%
D198
 
4.1%
B161
 
3.3%
Other values (14)1477
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4872
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A666
13.7%
N569
 
11.7%
I405
 
8.3%
L340
 
7.0%
E306
 
6.3%
T258
 
5.3%
C252
 
5.2%
R240
 
4.9%
D198
 
4.1%
B161
 
3.3%
Other values (14)1477
30.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4872
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A666
13.7%
N569
 
11.7%
I405
 
8.3%
L340
 
7.0%
E306
 
6.3%
T258
 
5.3%
C252
 
5.2%
R240
 
4.9%
D198
 
4.1%
B161
 
3.3%
Other values (14)1477
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A666
13.7%
N569
 
11.7%
I405
 
8.3%
L340
 
7.0%
E306
 
6.3%
T258
 
5.3%
C252
 
5.2%
R240
 
4.9%
D198
 
4.1%
B161
 
3.3%
Other values (14)1477
30.3%

def_int
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7937192118
Minimum0
Maximum5
Zeros761
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:43:51.608677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9340204987
Coefficient of variation (CV)1.176764383
Kurtosis1.466783604
Mean0.7937192118
Median Absolute Deviation (MAD)1
Skewness1.24728377
Sum1289
Variance0.872394292
MonotonicityNot monotonic
2022-09-02T01:43:51.789406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0761
46.9%
1555
34.2%
2219
 
13.5%
362
 
3.8%
425
 
1.5%
52
 
0.1%
ValueCountFrequency (%)
0761
46.9%
1555
34.2%
2219
 
13.5%
362
 
3.8%
425
 
1.5%
52
 
0.1%
ValueCountFrequency (%)
52
 
0.1%
425
 
1.5%
362
 
3.8%
2219
 
13.5%
1555
34.2%
0761
46.9%

def_int_td
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1519 
1
 
101
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01519
93.5%
1101
 
6.2%
24
 
0.2%

Length

2022-09-02T01:43:51.993294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:52.213882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01519
93.5%
1101
 
6.2%
24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01519
93.5%
1101
 
6.2%
24
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01519
93.5%
1101
 
6.2%
24
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01519
93.5%
1101
 
6.2%
24
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01519
93.5%
1101
 
6.2%
24
 
0.2%

sacks
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.328817734
Minimum0
Maximum10
Zeros203
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:43:52.355140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7280284
Coefficient of variation (CV)0.7420195984
Kurtosis1.055032911
Mean2.328817734
Median Absolute Deviation (MAD)1
Skewness0.931505453
Sum3782
Variance2.98608215
MonotonicityNot monotonic
2022-09-02T01:43:52.566675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1387
23.8%
2381
23.5%
3306
18.8%
0203
12.5%
4165
10.2%
5102
 
6.3%
642
 
2.6%
720
 
1.2%
810
 
0.6%
97
 
0.4%
ValueCountFrequency (%)
0203
12.5%
1387
23.8%
2381
23.5%
3306
18.8%
4165
10.2%
5102
 
6.3%
642
 
2.6%
720
 
1.2%
810
 
0.6%
97
 
0.4%
ValueCountFrequency (%)
101
 
0.1%
97
 
0.4%
810
 
0.6%
720
 
1.2%
642
 
2.6%
5102
 
6.3%
4165
10.2%
3306
18.8%
2381
23.5%
1387
23.8%

fumbles_rec
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
994 
1
482 
2
125 
3
 
20
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0994
61.2%
1482
29.7%
2125
 
7.7%
320
 
1.2%
43
 
0.2%

Length

2022-09-02T01:43:52.775935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:53.014519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0994
61.2%
1482
29.7%
2125
 
7.7%
320
 
1.2%
43
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0994
61.2%
1482
29.7%
2125
 
7.7%
320
 
1.2%
43
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0994
61.2%
1482
29.7%
2125
 
7.7%
320
 
1.2%
43
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0994
61.2%
1482
29.7%
2125
 
7.7%
320
 
1.2%
43
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0994
61.2%
1482
29.7%
2125
 
7.7%
320
 
1.2%
43
 
0.2%

fumbles_rec_td
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1550 
1
 
73
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01550
95.4%
173
 
4.5%
21
 
0.1%

Length

2022-09-02T01:43:53.672272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:53.880190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01550
95.4%
173
 
4.5%
21
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01550
95.4%
173
 
4.5%
21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01550
95.4%
173
 
4.5%
21
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01550
95.4%
173
 
4.5%
21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01550
95.4%
173
 
4.5%
21
 
0.1%

blocked_kick
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1561 
1
 
59
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01561
96.1%
159
 
3.6%
24
 
0.2%

Length

2022-09-02T01:43:54.043894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:54.251712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01561
96.1%
159
 
3.6%
24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01561
96.1%
159
 
3.6%
24
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01561
96.1%
159
 
3.6%
24
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01561
96.1%
159
 
3.6%
24
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01561
96.1%
159
 
3.6%
24
 
0.2%

safety
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1595 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01595
98.2%
129
 
1.8%

Length

2022-09-02T01:43:54.411968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:54.633489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01595
98.2%
129
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01595
98.2%
129
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01595
98.2%
129
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01595
98.2%
129
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01595
98.2%
129
 
1.8%

def_two_point_conv
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01624
100.0%

Length

2022-09-02T01:43:54.797648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:55.016506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01624
100.0%

Most occurring characters

ValueCountFrequency (%)
01624
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01624
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01624
100.0%

total_ret_td
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1585 
1
 
38
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01585
97.6%
138
 
2.3%
21
 
0.1%

Length

2022-09-02T01:43:55.195299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:55.416958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01585
97.6%
138
 
2.3%
21
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01585
97.6%
138
 
2.3%
21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01585
97.6%
138
 
2.3%
21
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01585
97.6%
138
 
2.3%
21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01585
97.6%
138
 
2.3%
21
 
0.1%

Team_abbrev
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct32
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size95.3 KiB
KAN
 
57
LAR
 
55
TAM
 
55
BUF
 
55
GNB
 
54
Other values (27)
1348 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4872
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHI
2nd rowGNB
3rd rowLAR
4th rowCAR
5th rowCLE

Common Values

ValueCountFrequency (%)
KAN57
 
3.5%
LAR55
 
3.4%
TAM55
 
3.4%
BUF55
 
3.4%
GNB54
 
3.3%
TEN54
 
3.3%
SFO54
 
3.3%
CIN53
 
3.3%
NOR52
 
3.2%
SEA52
 
3.2%
Other values (22)1083
66.7%

Length

2022-09-02T01:43:55.595855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kan57
 
3.5%
tam55
 
3.4%
buf55
 
3.4%
lar55
 
3.4%
gnb54
 
3.3%
ten54
 
3.3%
sfo54
 
3.3%
cin53
 
3.3%
nor52
 
3.2%
sea52
 
3.2%
Other values (22)1083
66.7%

Most occurring characters

ValueCountFrequency (%)
A666
13.7%
N569
 
11.7%
I405
 
8.3%
L340
 
7.0%
E306
 
6.3%
T258
 
5.3%
C252
 
5.2%
R240
 
4.9%
D198
 
4.1%
B161
 
3.3%
Other values (14)1477
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4872
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A666
13.7%
N569
 
11.7%
I405
 
8.3%
L340
 
7.0%
E306
 
6.3%
T258
 
5.3%
C252
 
5.2%
R240
 
4.9%
D198
 
4.1%
B161
 
3.3%
Other values (14)1477
30.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4872
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A666
13.7%
N569
 
11.7%
I405
 
8.3%
L340
 
7.0%
E306
 
6.3%
T258
 
5.3%
C252
 
5.2%
R240
 
4.9%
D198
 
4.1%
B161
 
3.3%
Other values (14)1477
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A666
13.7%
N569
 
11.7%
I405
 
8.3%
L340
 
7.0%
E306
 
6.3%
T258
 
5.3%
C252
 
5.2%
R240
 
4.9%
D198
 
4.1%
B161
 
3.3%
Other values (14)1477
30.3%

Opponent_abbrev
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct32
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size95.3 KiB
LAR
 
55
TAM
 
55
KAN
 
55
BUF
 
55
SFO
 
54
Other values (27)
1350 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4872
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGNB
2nd rowCHI
3rd rowCAR
4th rowLAR
5th rowTEN

Common Values

ValueCountFrequency (%)
LAR55
 
3.4%
TAM55
 
3.4%
KAN55
 
3.4%
BUF55
 
3.4%
SFO54
 
3.3%
GNB53
 
3.3%
TEN53
 
3.3%
BAL52
 
3.2%
NOR52
 
3.2%
SEA52
 
3.2%
Other values (22)1088
67.0%

Length

2022-09-02T01:43:55.817405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lar55
 
3.4%
kan55
 
3.4%
buf55
 
3.4%
tam55
 
3.4%
sfo54
 
3.3%
gnb53
 
3.3%
ten53
 
3.3%
cin52
 
3.2%
sea52
 
3.2%
nor52
 
3.2%
Other values (22)1088
67.0%

Most occurring characters

ValueCountFrequency (%)
A661
13.6%
N559
 
11.5%
I401
 
8.2%
L354
 
7.3%
E302
 
6.2%
R256
 
5.3%
T256
 
5.3%
C248
 
5.1%
D194
 
4.0%
B160
 
3.3%
Other values (14)1481
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4872
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A661
13.6%
N559
 
11.5%
I401
 
8.2%
L354
 
7.3%
E302
 
6.2%
R256
 
5.3%
T256
 
5.3%
C248
 
5.1%
D194
 
4.0%
B160
 
3.3%
Other values (14)1481
30.4%

Most occurring scripts

ValueCountFrequency (%)
Latin4872
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A661
13.6%
N559
 
11.5%
I401
 
8.2%
L354
 
7.3%
E302
 
6.2%
R256
 
5.3%
T256
 
5.3%
C248
 
5.1%
D194
 
4.0%
B160
 
3.3%
Other values (14)1481
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A661
13.6%
N559
 
11.5%
I401
 
8.2%
L354
 
7.3%
E302
 
6.2%
R256
 
5.3%
T256
 
5.3%
C248
 
5.1%
D194
 
4.0%
B160
 
3.3%
Other values (14)1481
30.4%

Opponent_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5067734
Minimum0
Maximum59
Zeros15
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:43:56.055725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q117
median24
Q330
95-th percentile41
Maximum59
Range59
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.14442611
Coefficient of variation (CV)0.4315533203
Kurtosis-0.1337410686
Mean23.5067734
Median Absolute Deviation (MAD)7
Skewness0.1411198889
Sum38175
Variance102.9093811
MonotonicityNot monotonic
2022-09-02T01:43:56.304776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17104
 
6.4%
2098
 
6.0%
2795
 
5.8%
2493
 
5.7%
3182
 
5.0%
2368
 
4.2%
3068
 
4.2%
1665
 
4.0%
1062
 
3.8%
3458
 
3.6%
Other values (45)831
51.2%
ValueCountFrequency (%)
015
 
0.9%
331
1.9%
51
 
0.1%
624
 
1.5%
730
1.8%
81
 
0.1%
932
2.0%
1062
3.8%
1111
 
0.7%
1214
 
0.9%
ValueCountFrequency (%)
591
 
0.1%
562
 
0.1%
551
 
0.1%
541
 
0.1%
531
 
0.1%
521
 
0.1%
514
0.2%
501
 
0.1%
492
 
0.1%
485
0.3%

points_allowed_0
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1609 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01609
99.1%
115
 
0.9%

Length

2022-09-02T01:43:56.552662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:56.760146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01609
99.1%
115
 
0.9%

Most occurring characters

ValueCountFrequency (%)
01609
99.1%
115
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01609
99.1%
115
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01609
99.1%
115
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01609
99.1%
115
 
0.9%

points_allowed_1_6
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1568 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01568
96.6%
156
 
3.4%

Length

2022-09-02T01:43:56.917527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:57.139333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01568
96.6%
156
 
3.4%

Most occurring characters

ValueCountFrequency (%)
01568
96.6%
156
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01568
96.6%
156
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01568
96.6%
156
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01568
96.6%
156
 
3.4%

points_allowed_7_13
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1417 
1
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01417
87.3%
1207
 
12.7%

Length

2022-09-02T01:43:57.314724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:57.567895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01417
87.3%
1207
 
12.7%

Most occurring characters

ValueCountFrequency (%)
01417
87.3%
1207
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01417
87.3%
1207
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01417
87.3%
1207
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01417
87.3%
1207
 
12.7%

points_allowed_14_20
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1251 
1
373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01251
77.0%
1373
 
23.0%

Length

2022-09-02T01:43:57.729459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:57.953623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01251
77.0%
1373
 
23.0%

Most occurring characters

ValueCountFrequency (%)
01251
77.0%
1373
 
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01251
77.0%
1373
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01251
77.0%
1373
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01251
77.0%
1373
 
23.0%

points_allowed_21_27
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1201 
1
423 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01201
74.0%
1423
 
26.0%

Length

2022-09-02T01:43:58.118119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:58.332976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01201
74.0%
1423
 
26.0%

Most occurring characters

ValueCountFrequency (%)
01201
74.0%
1423
 
26.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01201
74.0%
1423
 
26.0%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01201
74.0%
1423
 
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01201
74.0%
1423
 
26.0%

points_allowed_28_34
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1287 
1
337 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01287
79.2%
1337
 
20.8%

Length

2022-09-02T01:43:58.491189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:58.706222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01287
79.2%
1337
 
20.8%

Most occurring characters

ValueCountFrequency (%)
01287
79.2%
1337
 
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01287
79.2%
1337
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01287
79.2%
1337
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01287
79.2%
1337
 
20.8%

points_allowed_35
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.1 KiB
0
1411 
1
213 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01411
86.9%
1213
 
13.1%

Length

2022-09-02T01:43:58.872458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:43:59.081164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01411
86.9%
1213
 
13.1%

Most occurring characters

ValueCountFrequency (%)
01411
86.9%
1213
 
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01411
86.9%
1213
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
Common1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01411
86.9%
1213
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01411
86.9%
1213
 
13.1%

Total_DKP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.193349754
Minimum-4
Maximum37
Zeros93
Zeros (%)5.7%
Negative150
Negative (%)9.2%
Memory size12.8 KiB
2022-09-02T01:43:59.274441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile-2
Q12
median5
Q39
95-th percentile17.85
Maximum37
Range41
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.908800009
Coefficient of variation (CV)0.954055599
Kurtosis1.235607868
Mean6.193349754
Median Absolute Deviation (MAD)3
Skewness0.941563292
Sum10058
Variance34.91391755
MonotonicityNot monotonic
2022-09-02T01:43:59.489998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5142
 
8.7%
3135
 
8.3%
4130
 
8.0%
2123
 
7.6%
6104
 
6.4%
7102
 
6.3%
093
 
5.7%
192
 
5.7%
985
 
5.2%
882
 
5.0%
Other values (26)536
33.0%
ValueCountFrequency (%)
-431
 
1.9%
-326
 
1.6%
-229
 
1.8%
-164
3.9%
093
5.7%
192
5.7%
2123
7.6%
3135
8.3%
4130
8.0%
5142
8.7%
ValueCountFrequency (%)
371
 
0.1%
351
 
0.1%
301
 
0.1%
282
 
0.1%
271
 
0.1%
263
0.2%
252
 
0.1%
246
0.4%
236
0.4%
224
0.2%

Total_FDP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.193349754
Minimum-4
Maximum37
Zeros93
Zeros (%)5.7%
Negative150
Negative (%)9.2%
Memory size12.8 KiB
2022-09-02T01:43:59.716306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile-2
Q12
median5
Q39
95-th percentile17.85
Maximum37
Range41
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.908800009
Coefficient of variation (CV)0.954055599
Kurtosis1.235607868
Mean6.193349754
Median Absolute Deviation (MAD)3
Skewness0.941563292
Sum10058
Variance34.91391755
MonotonicityNot monotonic
2022-09-02T01:43:59.929614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5142
 
8.7%
3135
 
8.3%
4130
 
8.0%
2123
 
7.6%
6104
 
6.4%
7102
 
6.3%
093
 
5.7%
192
 
5.7%
985
 
5.2%
882
 
5.0%
Other values (26)536
33.0%
ValueCountFrequency (%)
-431
 
1.9%
-326
 
1.6%
-229
 
1.8%
-164
3.9%
093
5.7%
192
5.7%
2123
7.6%
3135
8.3%
4130
8.0%
5142
8.7%
ValueCountFrequency (%)
371
 
0.1%
351
 
0.1%
301
 
0.1%
282
 
0.1%
271
 
0.1%
263
0.2%
252
 
0.1%
246
0.4%
236
0.4%
224
0.2%

vis_team
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct32
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size95.3 KiB
TAM
 
58
LAR
 
58
SFO
 
56
SEA
 
54
MIN
 
54
Other values (27)
1344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4872
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGNB
2nd rowGNB
3rd rowLAR
4th rowLAR
5th rowTEN

Common Values

ValueCountFrequency (%)
TAM58
 
3.6%
LAR58
 
3.6%
SFO56
 
3.4%
SEA54
 
3.3%
MIN54
 
3.3%
BUF54
 
3.3%
TEN53
 
3.3%
CLE52
 
3.2%
ARI52
 
3.2%
PHI52
 
3.2%
Other values (22)1081
66.6%

Length

2022-09-02T01:44:00.166023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tam58
 
3.6%
lar58
 
3.6%
sfo56
 
3.4%
sea54
 
3.3%
min54
 
3.3%
buf54
 
3.3%
ten53
 
3.3%
gnb52
 
3.2%
bal52
 
3.2%
phi52
 
3.2%
Other values (22)1081
66.6%

Most occurring characters

ValueCountFrequency (%)
A663
13.6%
N552
 
11.3%
I408
 
8.4%
L351
 
7.2%
E305
 
6.3%
T260
 
5.3%
R252
 
5.2%
C251
 
5.2%
D196
 
4.0%
M160
 
3.3%
Other values (14)1474
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4872
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A663
13.6%
N552
 
11.3%
I408
 
8.4%
L351
 
7.2%
E305
 
6.3%
T260
 
5.3%
R252
 
5.2%
C251
 
5.2%
D196
 
4.0%
M160
 
3.3%
Other values (14)1474
30.3%

Most occurring scripts

ValueCountFrequency (%)
Latin4872
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A663
13.6%
N552
 
11.3%
I408
 
8.4%
L351
 
7.2%
E305
 
6.3%
T260
 
5.3%
R252
 
5.2%
C251
 
5.2%
D196
 
4.0%
M160
 
3.3%
Other values (14)1474
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A663
13.6%
N552
 
11.3%
I408
 
8.4%
L351
 
7.2%
E305
 
6.3%
T260
 
5.3%
R252
 
5.2%
C251
 
5.2%
D196
 
4.0%
M160
 
3.3%
Other values (14)1474
30.3%

home_team
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct32
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size95.3 KiB
KAN
 
65
BUF
 
56
GNB
 
55
TEN
 
54
CIN
 
54
Other values (27)
1340 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4872
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHI
2nd rowCHI
3rd rowCAR
4th rowCAR
5th rowCLE

Common Values

ValueCountFrequency (%)
KAN65
 
4.0%
BUF56
 
3.4%
GNB55
 
3.4%
TEN54
 
3.3%
CIN54
 
3.3%
NOR54
 
3.3%
PIT52
 
3.2%
LAR52
 
3.2%
BAL52
 
3.2%
TAM52
 
3.2%
Other values (22)1078
66.4%

Length

2022-09-02T01:44:00.370781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kan65
 
4.0%
buf56
 
3.4%
gnb55
 
3.4%
ten54
 
3.3%
cin54
 
3.3%
nor54
 
3.3%
pit52
 
3.2%
lar52
 
3.2%
bal52
 
3.2%
tam52
 
3.2%
Other values (22)1078
66.4%

Most occurring characters

ValueCountFrequency (%)
A664
13.6%
N576
 
11.8%
I398
 
8.2%
L343
 
7.0%
E303
 
6.2%
T254
 
5.2%
C249
 
5.1%
R244
 
5.0%
D196
 
4.0%
B163
 
3.3%
Other values (14)1482
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4872
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A664
13.6%
N576
 
11.8%
I398
 
8.2%
L343
 
7.0%
E303
 
6.2%
T254
 
5.2%
C249
 
5.1%
R244
 
5.0%
D196
 
4.0%
B163
 
3.3%
Other values (14)1482
30.4%

Most occurring scripts

ValueCountFrequency (%)
Latin4872
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A664
13.6%
N576
 
11.8%
I398
 
8.2%
L343
 
7.0%
E303
 
6.2%
T254
 
5.2%
C249
 
5.1%
R244
 
5.0%
D196
 
4.0%
B163
 
3.3%
Other values (14)1482
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A664
13.6%
N576
 
11.8%
I398
 
8.2%
L343
 
7.0%
E303
 
6.2%
T254
 
5.2%
C249
 
5.1%
R244
 
5.0%
D196
 
4.0%
B163
 
3.3%
Other values (14)1482
30.4%

vis_score
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.22105911
Minimum0
Maximum59
Zeros14
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:44:00.593452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q116
median23
Q330
95-th percentile41
Maximum59
Range59
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.18369905
Coefficient of variation (CV)0.4385544603
Kurtosis-0.2447490376
Mean23.22105911
Median Absolute Deviation (MAD)7
Skewness0.09173502736
Sum37711
Variance103.7077263
MonotonicityNot monotonic
2022-09-02T01:44:00.850209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17106
 
6.5%
2792
 
5.7%
3191
 
5.6%
2091
 
5.6%
3078
 
4.8%
2475
 
4.6%
2366
 
4.1%
1665
 
4.0%
2163
 
3.9%
1061
 
3.8%
Other values (40)836
51.5%
ValueCountFrequency (%)
014
 
0.9%
339
2.4%
52
 
0.1%
620
 
1.2%
738
2.3%
82
 
0.1%
941
2.5%
1061
3.8%
1112
 
0.7%
1216
 
1.0%
ValueCountFrequency (%)
592
 
0.1%
552
 
0.1%
512
 
0.1%
494
 
0.2%
486
0.4%
474
 
0.2%
462
 
0.1%
4512
0.7%
444
 
0.2%
4310
0.6%

home_score
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.85775862
Minimum0
Maximum56
Zeros16
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:44:01.104049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q117
median24
Q331
95-th percentile41
Maximum56
Range56
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.10597397
Coefficient of variation (CV)0.4235927664
Kurtosis-0.04338186116
Mean23.85775862
Median Absolute Deviation (MAD)7
Skewness0.1922704308
Sum38745
Variance102.1307099
MonotonicityNot monotonic
2022-09-02T01:44:01.363325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24109
 
6.7%
20106
 
6.5%
17102
 
6.3%
2799
 
6.1%
3171
 
4.4%
2370
 
4.3%
1370
 
4.3%
1666
 
4.1%
3464
 
3.9%
1063
 
3.9%
Other values (40)804
49.5%
ValueCountFrequency (%)
016
 
1.0%
322
 
1.4%
628
 
1.7%
722
 
1.4%
922
 
1.4%
1063
3.9%
1110
 
0.6%
1212
 
0.7%
1370
4.3%
1428
 
1.7%
ValueCountFrequency (%)
564
0.2%
542
 
0.1%
532
 
0.1%
522
 
0.1%
516
0.4%
502
 
0.1%
484
0.2%
476
0.4%
464
0.2%
458
0.5%

OT
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
False
1536 
True
 
88
ValueCountFrequency (%)
False1536
94.6%
True88
 
5.4%
2022-09-02T01:44:01.630060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Roof
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size106.3 KiB
outdoors
1121 
dome
253 
retractable roof (closed)
218 
retractable roof (open)
 
32

Length

Max length25
Median length8
Mean length9.954433498
Min length4

Characters and Unicode

Total characters16166
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoutdoors
2nd rowoutdoors
3rd rowoutdoors
4th rowoutdoors
5th rowoutdoors

Common Values

ValueCountFrequency (%)
outdoors1121
69.0%
dome253
 
15.6%
retractable roof (closed)218
 
13.4%
retractable roof (open)32
 
2.0%

Length

2022-09-02T01:44:01.803863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:44:02.051302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
outdoors1121
52.8%
dome253
 
11.9%
retractable250
 
11.8%
roof250
 
11.8%
closed218
 
10.3%
open32
 
1.5%

Most occurring characters

ValueCountFrequency (%)
o4366
27.0%
r1871
11.6%
t1621
 
10.0%
d1592
 
9.8%
s1339
 
8.3%
u1121
 
6.9%
e1003
 
6.2%
500
 
3.1%
a500
 
3.1%
l468
 
2.9%
Other values (8)1785
11.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15166
93.8%
Space Separator500
 
3.1%
Open Punctuation250
 
1.5%
Close Punctuation250
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o4366
28.8%
r1871
12.3%
t1621
 
10.7%
d1592
 
10.5%
s1339
 
8.8%
u1121
 
7.4%
e1003
 
6.6%
a500
 
3.3%
l468
 
3.1%
c468
 
3.1%
Other values (5)817
 
5.4%
Space Separator
ValueCountFrequency (%)
500
100.0%
Open Punctuation
ValueCountFrequency (%)
(250
100.0%
Close Punctuation
ValueCountFrequency (%)
)250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15166
93.8%
Common1000
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o4366
28.8%
r1871
12.3%
t1621
 
10.7%
d1592
 
10.5%
s1339
 
8.8%
u1121
 
7.4%
e1003
 
6.6%
a500
 
3.3%
l468
 
3.1%
c468
 
3.1%
Other values (5)817
 
5.4%
Common
ValueCountFrequency (%)
500
50.0%
(250
25.0%
)250
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o4366
27.0%
r1871
11.6%
t1621
 
10.0%
d1592
 
9.8%
s1339
 
8.3%
u1121
 
6.9%
e1003
 
6.2%
500
 
3.1%
a500
 
3.1%
l468
 
2.9%
Other values (8)1785
11.0%

Surface
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size101.4 KiB
grass
729 
grass
252 
fieldturf
185 
fieldturf
175 
astroturf
112 
Other values (3)
171 

Length

Max length10
Median length9
Mean length6.871305419
Min length5

Characters and Unicode

Total characters11159
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrass
2nd rowgrass
3rd rowgrass
4th rowgrass
5th rowgrass

Common Values

ValueCountFrequency (%)
grass729
44.9%
grass 252
 
15.5%
fieldturf 185
 
11.4%
fieldturf175
 
10.8%
astroturf112
 
6.9%
matrixturf90
 
5.5%
sportturf61
 
3.8%
a_turf20
 
1.2%

Length

2022-09-02T01:44:02.236043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T01:44:02.489973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
grass981
60.4%
fieldturf360
 
22.2%
astroturf112
 
6.9%
matrixturf90
 
5.5%
sportturf61
 
3.8%
a_turf20
 
1.2%

Most occurring characters

ValueCountFrequency (%)
s2135
19.1%
r1887
16.9%
a1203
10.8%
f1003
9.0%
g981
8.8%
t906
8.1%
u643
 
5.8%
i450
 
4.0%
437
 
3.9%
l360
 
3.2%
Other values (7)1154
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10702
95.9%
Space Separator437
 
3.9%
Connector Punctuation20
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s2135
19.9%
r1887
17.6%
a1203
11.2%
f1003
9.4%
g981
9.2%
t906
8.5%
u643
 
6.0%
i450
 
4.2%
l360
 
3.4%
d360
 
3.4%
Other values (5)774
 
7.2%
Space Separator
ValueCountFrequency (%)
437
100.0%
Connector Punctuation
ValueCountFrequency (%)
_20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10702
95.9%
Common457
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s2135
19.9%
r1887
17.6%
a1203
11.2%
f1003
9.4%
g981
9.2%
t906
8.5%
u643
 
6.0%
i450
 
4.2%
l360
 
3.4%
d360
 
3.4%
Other values (5)774
 
7.2%
Common
ValueCountFrequency (%)
437
95.6%
_20
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s2135
19.1%
r1887
16.9%
a1203
10.8%
f1003
9.0%
g981
8.8%
t906
8.1%
u643
 
5.8%
i450
 
4.0%
437
 
3.9%
l360
 
3.2%
Other values (7)1154
10.3%

Temperature
Real number (ℝ≥0)

HIGH CORRELATION

Distinct73
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.84913793
Minimum7
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:44:02.717620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile35
Q152
median72
Q372
95-th percentile82
Maximum93
Range86
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.29407842
Coefficient of variation (CV)0.2433458743
Kurtosis-0.1102710526
Mean62.84913793
Median Absolute Deviation (MAD)8
Skewness-0.7729285215
Sum102067
Variance233.9088347
MonotonicityNot monotonic
2022-09-02T01:44:02.986901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72551
33.9%
4834
 
2.1%
5232
 
2.0%
8130
 
1.8%
5429
 
1.8%
4327
 
1.7%
7627
 
1.7%
4626
 
1.6%
7726
 
1.6%
6425
 
1.5%
Other values (63)817
50.3%
ValueCountFrequency (%)
72
 
0.1%
114
0.2%
142
 
0.1%
152
 
0.1%
232
 
0.1%
242
 
0.1%
256
0.4%
262
 
0.1%
282
 
0.1%
292
 
0.1%
ValueCountFrequency (%)
934
 
0.2%
914
 
0.2%
902
 
0.1%
892
 
0.1%
888
0.5%
8714
0.9%
868
0.5%
8518
1.1%
846
 
0.4%
8314
0.9%

Humidity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct85
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.68965517
Minimum0
Maximum100
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:44:03.277068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q145
median48
Q368
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.05183259
Coefficient of variation (CV)0.3061938979
Kurtosis0.01068381292
Mean55.68965517
Median Absolute Deviation (MAD)8
Skewness0.4094830317
Sum90440
Variance290.7649946
MonotonicityNot monotonic
2022-09-02T01:44:03.569122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45545
33.6%
5634
 
2.1%
4630
 
1.8%
5229
 
1.8%
5828
 
1.7%
6627
 
1.7%
6726
 
1.6%
6026
 
1.6%
5724
 
1.5%
5924
 
1.5%
Other values (75)831
51.2%
ValueCountFrequency (%)
02
 
0.1%
72
 
0.1%
82
 
0.1%
94
0.2%
102
 
0.1%
112
 
0.1%
122
 
0.1%
132
 
0.1%
146
0.4%
194
0.2%
ValueCountFrequency (%)
1004
 
0.2%
994
 
0.2%
972
 
0.1%
952
 
0.1%
944
 
0.2%
9313
0.8%
9210
0.6%
9114
0.9%
9012
0.7%
8912
0.7%

Wind_Speed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.810960591
Minimum0
Maximum35
Zeros593
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:44:03.824687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile17
Maximum35
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.987714106
Coefficient of variation (CV)1.030417263
Kurtosis0.5066192504
Mean5.810960591
Median Absolute Deviation (MAD)5
Skewness0.9168486972
Sum9437
Variance35.85272021
MonotonicityNot monotonic
2022-09-02T01:44:04.065138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0593
36.5%
8110
 
6.8%
795
 
5.8%
488
 
5.4%
683
 
5.1%
571
 
4.4%
967
 
4.1%
1063
 
3.9%
1261
 
3.8%
1160
 
3.7%
Other values (18)333
20.5%
ValueCountFrequency (%)
0593
36.5%
124
 
1.5%
232
 
2.0%
346
 
2.8%
488
 
5.4%
571
 
4.4%
683
 
5.1%
795
 
5.8%
8110
 
6.8%
967
 
4.1%
ValueCountFrequency (%)
352
 
0.1%
272
 
0.1%
252
 
0.1%
244
 
0.2%
238
 
0.5%
228
 
0.5%
216
 
0.4%
205
 
0.3%
1924
1.5%
1812
0.7%

Vegas_Line
Real number (ℝ)

Distinct39
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.678263547
Minimum-22
Maximum0
Zeros4
Zeros (%)0.2%
Negative1620
Negative (%)99.8%
Memory size12.8 KiB
2022-09-02T01:44:04.322834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-22
5-th percentile-13
Q1-7.5
median-4.5
Q3-3
95-th percentile-1
Maximum0
Range22
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.708588781
Coefficient of variation (CV)-0.6531202278
Kurtosis1.355280922
Mean-5.678263547
Median Absolute Deviation (MAD)2
Skewness-1.180621045
Sum-9221.5
Variance13.75363075
MonotonicityNot monotonic
2022-09-02T01:44:04.565054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
-3214
13.2%
-3.5165
 
10.2%
-7112
 
6.9%
-2.5111
 
6.8%
-1101
 
6.2%
-490
 
5.5%
-7.585
 
5.2%
-6.577
 
4.7%
-672
 
4.4%
-5.567
 
4.1%
Other values (29)530
32.6%
ValueCountFrequency (%)
-222
 
0.1%
-20.52
 
0.1%
-202
 
0.1%
-184
 
0.2%
-17.56
0.4%
-176
0.4%
-16.512
0.7%
-162
 
0.1%
-15.54
 
0.2%
-154
 
0.2%
ValueCountFrequency (%)
04
 
0.2%
-1101
6.2%
-1.548
 
3.0%
-240
 
2.5%
-2.5111
6.8%
-3214
13.2%
-3.5165
10.2%
-490
5.5%
-4.546
 
2.8%
-551
 
3.1%

Vegas_Favorite
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)2.0%
Missing4
Missing (%)0.2%
Memory size95.3 KiB
KAN
 
102
LAR
 
86
GNB
 
83
TAM
 
80
BAL
 
78
Other values (28)
1191 

Length

Max length19
Median length3
Mean length3.059259259
Min length3

Characters and Unicode

Total characters4956
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHI
2nd rowCHI
3rd rowLAR
4th rowLAR
5th rowCLE

Common Values

ValueCountFrequency (%)
KAN102
 
6.3%
LAR86
 
5.3%
GNB83
 
5.1%
TAM80
 
4.9%
BAL78
 
4.8%
NWE74
 
4.6%
NOR72
 
4.4%
DAL72
 
4.4%
SFO70
 
4.3%
BUF70
 
4.3%
Other values (23)833
51.3%

Length

2022-09-02T01:44:05.519760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kan102
 
6.2%
lar86
 
5.3%
gnb83
 
5.1%
tam80
 
4.9%
bal78
 
4.8%
nwe74
 
4.5%
nor72
 
4.4%
dal72
 
4.4%
buf70
 
4.3%
sfo70
 
4.3%
Other values (25)845
51.8%

Most occurring characters

ValueCountFrequency (%)
A716
14.4%
N592
11.9%
L420
 
8.5%
I349
 
7.0%
E313
 
6.3%
R282
 
5.7%
T239
 
4.8%
B231
 
4.7%
C217
 
4.4%
O177
 
3.6%
Other values (24)1420
28.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4872
98.3%
Lowercase Letter60
 
1.2%
Space Separator12
 
0.2%
Connector Punctuation6
 
0.1%
Dash Punctuation6
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A716
14.7%
N592
12.2%
L420
 
8.6%
I349
 
7.2%
E313
 
6.4%
R282
 
5.8%
T239
 
4.9%
B231
 
4.7%
C217
 
4.5%
O177
 
3.6%
Other values (14)1336
27.4%
Lowercase Letter
ValueCountFrequency (%)
a12
20.0%
b12
20.0%
e12
20.0%
t6
10.0%
v6
10.0%
r6
10.0%
m6
10.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4932
99.5%
Common24
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A716
14.5%
N592
12.0%
L420
 
8.5%
I349
 
7.1%
E313
 
6.3%
R282
 
5.7%
T239
 
4.8%
B231
 
4.7%
C217
 
4.4%
O177
 
3.6%
Other values (21)1396
28.3%
Common
ValueCountFrequency (%)
12
50.0%
_6
25.0%
-6
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A716
14.4%
N592
11.9%
L420
 
8.5%
I349
 
7.0%
E313
 
6.3%
R282
 
5.7%
T239
 
4.8%
B231
 
4.7%
C217
 
4.4%
O177
 
3.6%
Other values (24)1420
28.7%

Over_Under
Real number (ℝ≥0)

HIGH CORRELATION

Distinct44
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.59421182
Minimum35
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T01:44:05.760226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile40
Q143.5
median46.5
Q349.5
95-th percentile54.5
Maximum58
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.261954344
Coefficient of variation (CV)0.09146960915
Kurtosis-0.2980141878
Mean46.59421182
Median Absolute Deviation (MAD)3
Skewness0.1285469793
Sum75669
Variance18.16425483
MonotonicityNot monotonic
2022-09-02T01:44:06.011392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4494
 
5.8%
4788
 
5.4%
4876
 
4.7%
46.574
 
4.6%
4673
 
4.5%
4971
 
4.4%
4570
 
4.3%
4369
 
4.2%
44.566
 
4.1%
49.563
 
3.9%
Other values (34)880
54.2%
ValueCountFrequency (%)
352
 
0.1%
36.58
0.5%
3714
0.9%
37.512
0.7%
388
0.5%
38.56
 
0.4%
3910
0.6%
39.517
1.0%
4016
1.0%
40.514
0.9%
ValueCountFrequency (%)
582
 
0.1%
57.52
 
0.1%
56.512
0.7%
564
 
0.2%
55.520
1.2%
5522
1.4%
54.528
1.7%
5416
1.0%
53.519
1.2%
5312
0.7%

game_date
Categorical

HIGH CARDINALITY

Distinct174
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size106.4 KiB
2021-01-03
 
32
2019-12-29
 
31
2022-01-02
 
30
2021-09-12
 
28
2021-10-03
 
28
Other values (169)
1475 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters16240
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2019-09-05
2nd row2019-09-05
3rd row2019-09-08
4th row2019-09-08
5th row2019-09-08

Common Values

ValueCountFrequency (%)
2021-01-0332
 
2.0%
2019-12-2931
 
1.9%
2022-01-0230
 
1.8%
2021-09-1228
 
1.7%
2021-10-0328
 
1.7%
2020-09-2028
 
1.7%
2020-09-2728
 
1.7%
2022-01-0928
 
1.7%
2021-10-1028
 
1.7%
2020-12-1328
 
1.7%
Other values (164)1335
82.2%

Length

2022-09-02T01:44:06.248383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-0332
 
2.0%
2019-12-2931
 
1.9%
2022-01-0230
 
1.8%
2021-09-1228
 
1.7%
2021-10-0328
 
1.7%
2020-09-2028
 
1.7%
2020-09-2728
 
1.7%
2022-01-0928
 
1.7%
2021-10-1028
 
1.7%
2020-12-1328
 
1.7%
Other values (164)1335
82.2%

Most occurring characters

ValueCountFrequency (%)
24008
24.7%
03602
22.2%
13402
20.9%
-3248
20.0%
91005
 
6.2%
3225
 
1.4%
7173
 
1.1%
5162
 
1.0%
8155
 
1.0%
6137
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12992
80.0%
Dash Punctuation3248
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
24008
30.8%
03602
27.7%
13402
26.2%
91005
 
7.7%
3225
 
1.7%
7173
 
1.3%
5162
 
1.2%
8155
 
1.2%
6137
 
1.1%
4123
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
-3248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common16240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
24008
24.7%
03602
22.2%
13402
20.9%
-3248
20.0%
91005
 
6.2%
3225
 
1.4%
7173
 
1.1%
5162
 
1.0%
8155
 
1.0%
6137
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII16240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24008
24.7%
03602
22.2%
13402
20.9%
-3248
20.0%
91005
 
6.2%
3225
 
1.4%
7173
 
1.1%
5162
 
1.0%
8155
 
1.0%
6137
 
0.8%

Interactions

2022-09-02T01:43:46.218117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:16.431988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:19.445294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:21.875599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:24.713780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:27.104366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:29.444836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:33.451117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:35.858420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:38.179031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:40.719690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:43.733998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:46.429883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:16.649810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:19.644630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:22.078026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:24.914073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:27.300740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:29.801964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:33.651211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:36.071228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:38.388026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:40.943899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:43.960673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:46.622687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:16.845048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:19.842842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:22.295575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:25.114183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:27.492235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:30.271194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:33.849548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:36.265420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:38.589191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:41.173884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:44.186860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:46.825050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:17.048851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:20.059297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:22.521396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:25.321275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:27.694108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:30.480448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:34.056554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:36.468311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:38.806874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:41.824638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:44.399557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:47.014389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:17.501979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:20.257412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:22.736382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:25.516110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:27.884357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:30.709866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:34.258247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:36.652364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:38.996077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:42.025239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:44.596598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:47.219493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:17.690425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:20.447833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:22.935405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:25.717131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:28.065697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:31.281502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:34.446581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:36.844578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:39.215879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:42.238838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:44.796317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:47.427908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:17.896101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:20.668056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:23.162698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:25.922792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:28.267938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:31.508643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:34.648881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:37.054000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:39.430242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:42.462311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:45.011173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:47.619911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:18.098368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:20.861955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:23.381254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:26.136078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:28.461455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:31.710947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:34.850707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:37.241477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:39.643514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:42.661189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:45.234407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:47.804854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:18.297082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:21.060483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:23.869911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:26.323183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:28.647323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:32.637398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:35.051622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:37.413899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:39.850192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:42.866443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:45.422070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:48.012614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:18.507102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:21.262523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:24.080373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:26.534136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:28.849153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:32.835153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:35.261389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:37.601046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:40.073916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:43.091037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:45.624003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:48.213553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:18.722237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:21.476376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:24.295994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:26.733702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:29.063207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:33.049224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:35.468553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:37.792671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:40.299832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:43.316164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:45.828186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:48.417094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:19.181394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:21.679288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:24.504617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:26.919942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:29.254594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:33.248976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:35.669369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:37.977140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:40.504757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:43.529535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T01:43:46.020955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-02T01:44:06.477080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-02T01:44:06.915663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-02T01:44:07.355758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-02T01:44:07.807784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-02T01:44:08.201636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-02T01:43:48.865051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-02T01:43:50.257331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-02T01:43:50.659830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

game_idteamdef_intdef_int_tdsacksfumbles_recfumbles_rec_tdblocked_kicksafetydef_two_point_convtotal_ret_tdTeam_abbrevOpponent_abbrevOpponent_scorepoints_allowed_0points_allowed_1_6points_allowed_7_13points_allowed_14_20points_allowed_21_27points_allowed_28_34points_allowed_35Total_DKPTotal_FDPvis_teamhome_teamvis_scorehome_scoreOTRoofSurfaceTemperatureHumidityWind_SpeedVegas_LineVegas_FavoriteOver_Undergame_date
0201909050chiCHI005000000CHIGNB10001000099GNBCHI103Falseoutdoorsgrass656910-3.5CHI47.02019-09-05
1201909050chiGNB105000000GNBCHI301000001414GNBCHI103Falseoutdoorsgrass656910-3.5CHI47.02019-09-05
2201909080carLAR103200000LARCAR27000010099LARCAR3027Falseoutdoorsgrass87533-1.5LAR49.52019-09-08
3201909080carCAR101001000CARLAR30000001044LARCAR3027Falseoutdoorsgrass87533-1.5LAR49.52019-09-08
4201909080cleCLE004000000CLETEN43000000100TENCLE4313Falseoutdoorsgrass715510-5.5CLE44.02019-09-08
5201909080cleTEN315000100TENCLE1300100002323TENCLE4313Falseoutdoorsgrass715510-5.5CLE44.02019-09-08
6201909080crdDET105000000DETARI27000010077DETARI2727Trueretractable roof (closed)grass72450-2.5DET45.52019-09-08
7201909080crdARI003100000ARIDET27000010055DETARI2727Trueretractable roof (closed)grass72450-2.5DET45.52019-09-08
8201909080dalDAL001200000DALNYG17000100066NYGDAL1735Falseretractable roof (closed)fieldturf72450-7.0DAL44.02019-09-08
9201909080dalNYG000000000NYGDAL350000001-4-4NYGDAL1735Falseretractable roof (closed)fieldturf72450-7.0DAL44.02019-09-08

Last rows

game_idteamdef_intdef_int_tdsacksfumbles_recfumbles_rec_tdblocked_kicksafetydef_two_point_convtotal_ret_tdTeam_abbrevOpponent_abbrevOpponent_scorepoints_allowed_0points_allowed_1_6points_allowed_7_13points_allowed_14_20points_allowed_21_27points_allowed_28_34points_allowed_35Total_DKPTotal_FDPvis_teamhome_teamvis_scorehome_scoreOTRoofSurfaceTemperatureHumidityWind_SpeedVegas_LineVegas_FavoriteOver_Undergame_date
1614202201230kanBUF002000000BUFKAN420000001-2-2BUFKAN3642Trueoutdoorsgrass35546-2.0KAN54.52022-01-23
1615202201230kanKAN002000000KANBUF360000001-2-2BUFKAN3642Trueoutdoorsgrass35546-2.0KAN54.52022-01-23
1616202201230tamLAR103100000LARTAM27000010077LARTAM3027Falseoutdoorsgrass506911-3.0TAM48.02022-01-23
1617202201230tamTAM002300000TAMLAR30000001077LARTAM3027Falseoutdoorsgrass506911-3.0TAM48.02022-01-23
1618202201300kanCIN204000000CINKAN24000010088CINKAN2724Trueoutdoorsgrass41414-7.0KAN55.02022-01-30
1619202201300kanKAN101000000KANCIN27000010033CINKAN2724Trueoutdoorsgrass41414-7.0KAN55.02022-01-30
1620202201300ramLAR100000000LARSFO17000100033SFOLAR1720Falsedomematrixturf72450-3.5LAR46.02022-01-30
1621202201300ramSFO102000000SFOLAR20000100055SFOLAR1720Falsedomematrixturf72450-3.5LAR46.02022-01-30
1622202202130cinCIN202000000CINLAR23000010066LARCIN2320Falsedomematrixturf72450-4.0LAR48.52022-02-13
1623202202130cinLAR007000000LARCIN20000100088LARCIN2320Falsedomematrixturf72450-4.0LAR48.52022-02-13